Comparison of Grammatical Error Correction Using Back-Translation Models

Aomi Koyama, Kengo Hotate, Masahiro Kaneko, Mamoru Komachi


Abstract
Grammatical error correction (GEC) suffers from a lack of sufficient parallel data. Studies on GEC have proposed several methods to generate pseudo data, which comprise pairs of grammatical and artificially produced ungrammatical sentences. Currently, a mainstream approach to generate pseudo data is back-translation (BT). Most previous studies using BT have employed the same architecture for both the GEC and BT models. However, GEC models have different correction tendencies depending on the architecture of their models. Thus, in this study, we compare the correction tendencies of GEC models trained on pseudo data generated by three BT models with different architectures, namely, Transformer, CNN, and LSTM. The results confirm that the correction tendencies for each error type are different for every BT model. In addition, we investigate the correction tendencies when using a combination of pseudo data generated by different BT models. As a result, we find that the combination of different BT models improves or interpolates the performance of each error type compared with using a single BT model with different seeds.
Anthology ID:
2021.naacl-srw.16
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
126–135
Language:
URL:
https://aclanthology.org/2021.naacl-srw.16
DOI:
10.18653/v1/2021.naacl-srw.16
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-srw.16.pdf